A spatially-varying relaxation parameter Lattice Boltzmann Method (SVRP-LBM) for predicting the effective thermal conductivity of composite material

Functional filler-reinforced composite materials play critical roles in thermal management in various engineering applications. In this study, an in-house coded spatially-varying relaxation parameter Lattice Boltzmann Method (SVRP-LBM) solver has been developed for predicting the effective thermal conductivity (ETC) of simulated composite materials. A randomly dispersed filler generator (RDFG) incorporating Monte Carlo random sampling method has been developed for reconstructing the microstructure of composite materials. The artificial composite materials with functional fillers of different geometries and particle size are studied. The SVRP-LBM is validated against FVM perditions and theoretical models. The spatially-varying relaxation parameters method has been used to reflect materials with different thermophysical properties, including the interfacial contact resistance between the matrix-filler interfaces. It is demonstrated that the lowest relaxation parameters should be around 1.0 in order to achieve a higher accuracy of LBM predictions. The effects of filler geometry and particle sizes on the ETC are also assessed. The shape and orientation of the anisotropic filler have strong effects on the ETC. After the geometry of the filler in the numerical models being adjusted accordingly to the real fillers, the predictions show good agreement with experimental data. All in all, the SVRP-LBM solver has shown good capability and accuracy for predicting the ETC of composite material.</p

Coupling machine learning with thermodynamic modelling to develop a composition-property model for alkali-activated materials

Alkali-activation is one of the most promising routes for utilisation of versatile aluminosilicate resources. However, the variations of chemical compositions in these resources have increased the challenge of designing alkali-activated materials (AAMs) with multiple sources, posing the demand for establishing composition-property correlations that can represent a wide range of AAMs. This study proposes a data-driven approach to develop such composition-property correlations combining machine learning with global sensitivity analysis and thermodynamic modelling. The strength performance of alkali-activated concretes was investigated for a benchmark study (196 data inputs). The impact of the five key chemical compositions, CaO–SiO2–Al2O3–MgO–Na2O, has been assessed. The results show that despite the use of different aluminosilicate precursors, there appear to be coherent connections between bulk binder chemical compositions, phase assemblages, and the performance of AAMs. The composition-property correlations established via machine learning can be used to facilitate the on-demand design of AAMs utilising varying aluminosilicate resources.</p

Fisheye-Lens-Based Visual Sun Compass for Perception of Spatial Orientation

In complex aeronautical and space engineering systems, conventional sensors used for environment perception fail to determine the orientation because of the influences of the special environments, wherein geomagnetic fields are exceptional and the Global Positioning System is unavailable. This paper presents a fisheye-lens-based visual sun compass that is efficient in determining orientation in such applications. The mathematical model is described, and the absolute orientation is identified by image processing techniques. For robust detection of the sun in the image of the visual sun compass, a modified maximally stable extremal region algorithm and a method named constrained least squares with pruning are proposed. In comparison with conventional sensors, the proposed visual sun compass can provide absolute orientation with a tiny size and light weight in especial environments. Experiments are carried out with a prototype validating the efficiency of the proposed visual sun compass